Exposure to Black Carbon (BC) and the Secondary Aerosol (p-SO42− and p-NO3−) Components of Fine Particulate Matter (PM2.5), and Cardiopulmonary Morbidity in Jeddah, Saudi Arabia

Abstract

This study assessed the morbidity risk for respiratory (RDs) and cardiovascular diseases (CVDs) from the exposure to black carbon (BC), p-SO₄²⁻, and p-NO₃⁻ measured in PM_2.5 collected from Jeddah. The 24 h PM_2.5 sampling was done at three sites for 6 weeks quarterly from 8 April 2013 to 18 February 2014, with concurrent hospital data collection. Though the data were collected more than a decade ago, the results are still applicable to the current middle east region and beyond. The relative risk (RR) for CVDs and RDs was computed in a single pollutant GLM time-series model using moving averages (MAs) and presented with 95% confidence intervals (CI). Overall, the morbidity risk was highest in outpatients. The risk of RDs was highest in younger age groups with age group 0–14 years being the most vulnerable, while the risk of CVDs was highest in age groups above 30 years. BC: The risk of RDs was highest in outpatient females of 0–14 years at all MAs; 1.483 (1.035, 2.123) to 2.501 (1.522, 4.112), and males of 31–45 years; 1.758 (1.134, 2.725) to 1.895 (1.142, 3.145) at MAs 5–7. The risk of CVDs was highest in age group 61–75 years but with a marginal statistical significance. p-SO₄²⁻: The risk of RDs was highest in males of 31–45 years; 1.173 (1.066, 1.291) to 1.256 (1.126, 1.400) at MAs_3–7. The risk of CVDs was also highest in age group 31–45 years; 1.078 (1.004, 1.157) to 1.090 (1.010, 1.177), with higher risk estimates in males; 1.084 (0.986, 1.191) to 1.120 (1.013, 1.239) at MAs 4–7. p-NO₃⁻: No significant risk was observed for RD morbidity. The overall risk of CVDs was significant in outpatients with an age group of 61–75 years at all MAs; 1.204 (1.014, 1.429) to 1.316 (1.096, 1.579); females, 1.221 (0.987, 1.511) to 1.428 (1.123, 1.816); and males, 1.205 (1.022, 1.421) to 1.258 (1.052, 1.505). This is the first study to assess the cardiopulmonary risk from BC, p-SO₄²⁻, and p-NO₃⁻ exposure in Jeddah. A high morbidity risk was observed in all age groups, accentuating the effects of elevated air pollution in Saudi Arabia and beyond. Females of 0–14 years were more at risk for RDs morbidity while the CVDs morbidity risk was relatively higher in males in age groups above 30 years.

1. Introduction

Fine particulate matter (PM_2.5) is a diverse mixture of pollutants with highly variable physicochemical properties [1]. Black carbon (BC) comprises a part of the PM_2.5 generated primarily from industrial and traffic emissions, produced as a combustion by-product of biomass, fossil fuels, diesel, and gasoline [2,3,4,5]. More than 90% of BC forms as fine aerosols and may make up to 15% of the urban PM_2.5 [6]. Several studies have linked the exposure to traffic-related air pollution, particularly ambient BC, with adverse cardiopulmonary health outcomes [7,8,9,10,11,12,13]. In children, BC has been shown to significantly increase the risk of pulmonary infections, mostly allergic reactions such as asthma and rhinitis [8,13,14] and acute respiratory inflammation [15].

Particulate sulfate (p-SO₄²⁻) and nitrate (p-NO₃⁻) comprise a part of the secondary aerosol portion of PM_2.5, formed by a gas-phase oxidation of the gaseous oxides of sulfur and nitrogen (SO₂ and NO_X) into sulfuric (H₂SO₄) and nitric acid (HNO₃), respectively [16]. These pollutants exist as ammonium salts [(NH₄)₂SO₄, NH₄HSO₄, and NH₄NO₃] formed by the neutralization of H₂SO₄ and HNO₃ by atmospheric ammonia [17,18]. These pollutants accounted for a significant portion of Jeddah’s PM_2.5 (BC, 3.6–7.2%, and secondary aerosols, 36.4–48.1%) [19], and are toxic to human health and the environment. Jeddah city is heavily industrialized and one of the busiest cities in Saudi Arabia with high automobile traffic. Thus, the high anthropogenic emissions of particulate air pollutants have resulted in a substantial deterioration of the air quality over the years.

Several studies and reports from the World Bank (WB), and World Health Organization (WHO) have indicated that air pollution remains a major human health threat, accounting for an estimated 7 million premature deaths annually due to diseases like heart disease, stroke, lung cancer, and respiratory infections, with a significant burden in heavily industrialized low- and middle-income countries [20,21,22,23]. A majority of the studies on air pollution epidemiology have been carried out in the developed countries of Europe [24,25,26] and North America [27,28,29,30,31]. Only a few studies have been done in the Middle East [18,32,33,34,35]. This scarcity of research underscores a significant knowledge gap and emphasizes the urgent need for more comprehensive air pollution epidemiology studies in Saudi Arabia and across the broader Middle East region.

Chronic non-communicable diseases are responsible for approximately 78% of the 90,000 annual deaths in Saudi Arabia, with cardiovascular diseases accounting for 46% and respiratory diseases for 2% of these fatalities [36]. Increasing trends in cardiopulmonary morbidity in Saudi Arabia have been recently reported among children ≤14 years and young adults of 25–45 years [18,34]. While the cause of these trends may not be clear, these studies have shown a clear pattern between elevated levels of air pollution and an increased risk of cardiopulmonary morbidity. Thus, the contribution of elevated air pollution to the current health dynamics in Saudi Arabia is significant. Also, Saudi Arabia’s population pyramid shows an exponentially increasing mortality rate among people below 40 years. While there are several causes of this index, the contribution from particulate air pollution may be significant.

In this study, we aimed to assess the influence of the exposure to BC and secondary aerosols (p-SO₄²⁻ and p-NO₃⁻) measured from PM_2.5 on cardiopulmonary morbidity. The results will supplement our previously published data [34] and further refine the association between elevated PM_2.5 and the current trends in cardiovascular and respiratory morbidity in Saudi Arabia. This is key to the formulation of new and improved policies on particulate air pollution in the Middle East Region.

2. Materials and Methods

2.1. Study Area

About 83% of the Saudi population (31.54 million) is urban, with 55.2% male and 44.8% female [37,38]. Jeddah (Figure S1) is located on the west coast of Saudi Arabia (Lat. 29.2 North and Lon. 39.7 East), on the midway eastern shore of the Red Sea in the Makkah province. With a municipality area of 5460 km² and a population of 3.58 million [39], Jeddah is the second-largest city in Saudi Arabia after Riyadh. The city features a predominantly hot and arid climate with a warm winter (15 °C at dawn to 28 °C in the afternoon) and a hot summer with temperatures exceeding 40 °C in the afternoon and dropping to 30s °C in the evening. The summer humidity is high due to high sea water temperatures and becomes the lowest in winter. Jeddah receives minimal rainfall during the months of November and December. Winter and spring are mostly characterized by gusty winds occasionally accompanied by sandstorms, thunderstorms, and heavy rains.

2.2. PM_2.5 Sample Collection and Analysis

The 24 h PM_2.5 sampling was conducted at 3 fixed sites in Jeddah (Figure S1) for 6 weeks per quarter of the year, starting from 8 April 2013 to 18 February 2014, with each representing a sampling cycle. Cycle_1 (Spring), 8 April–28 May 2013; cycle_2 (Summer), 11 July–24 August 2013; cycle_3 (Fall), 2 October–16 November 2013; and cycle_4 (Winter), 7 January 2014–18 February 2014. Collectively, the four cycles comprised a year of PM_2.5 sampling. More details of the PM_2.5 sampling and analyses for BC and secondary ions have been previously published [40]. The 24 h PM_2.5 collected from different sampling sites (industrial and residential), were averaged for a more representative estimate of the exposure. Residential sites were densely populated areas while the heavy traffic and industrial areas were presented as industrial sites. As shown in Table 1, the mean 24 h PM_2.5 varied significantly by season, and the mean daily temperature was relatively stable across all seasons, while relative humidity (RH) and wind speed (WS) varied significantly throughout the study period [19]. There were only a few days with PM data missing, and this did not have any significant effect on statistical analyses.

2.3. Hospital Data

The medical staff at King Abdulaziz, King Abdulaziz University and Soliman Fakeeh Hospitals collected data on patients who visited the emergency room (ER) and were outpatients and or admitted to the hospital as inpatients due to either cardiovascular (CVDs) or respiratory diseases (RDs) or both, for the period of 8 April 2013, to 18 February 2014. ER visits comprised all patients who came to the hospital ER department without any prior doctor’s appointment. Outpatients received treatment at the hospital emergency room but were not admitted. This category also included those patients who regularly visited the hospital on a doctor’s appointment. Such patients would have chronic cardiopulmonary illnesses that required them to see a doctor regularly. They received medical care but were not admitted to the hospital. Inpatients were comprised of those patients that got at least an overnight admission to the hospital. These were initially either ER patients or Outpatients that were deemed to be in worse conditions and needed constant supervision by a healthcare provider.

Hospital data were simultaneously collected with PM_2.5 for each day of sampling. Patient data included the date of admission or hospital visit, patient age, gender, nationality, diagnosis, and their ICD-10 code. All the patient records were coded with patient IDs to ensure confidentiality. Institutional Review Board (IRB) approvals to use the hospital data were obtained from Saudi Arabia’s Ministry of Health, University at Albany, SUNY and New York State Department of Health IRB committees. Missing data were defined as those patient records for which either gender, patient category, and age were not assigned, or the corresponding PM_2.5 data was missing. There were only a few missing data, and these did not show statistical significance on the observed associations.

To adequately capture the community-wide burden of morbidity and reduce the potential for introducing selection bias, we selected the sampling sites to have a good balance between industrial and residential areas and only collected hospital data from hospitals that were within the sampling area and the surroundings. Also, as noted in Section 2.1, most Jeddah’s population is urban (about 83%), so there is a good level of confidence that the hospital data was significantly representative with minimal selection bias issues.

2.4. Statistical Analysis

We applied a generalized linear time-series model with negative binomial distribution to assess the short-term effects of BC, p-SO₄²⁻, and p-NO₃⁻ measured from PM_2.5 on cardiopulmonary morbidity in Jeddah. The model is only used for outcomes due to short-term (up to a week) variations in exposure. The effects observed with long-term environmental exposures may hardly be distinguishable from seasonal trend effects [41,42]. Cardiopulmonary outcomes have previously been shown to be either nearly contemporaneous or take several days following exposure to air pollution [29]. Thus, we assessed the exposure–outcome association based on moving averages (MAs) to show the trend of the risk over time. MAs were computed by averaging the individual time lags up to six days (lag 0–lag 6) post exposure as follows: MA_2 (2 days MA) = average of lags (0–1), MA_3 (3 days MA) = average of lags (0–2), up to MA_7 = average of lags (0–6), where “lag 0 effects” means the effects on the same day of exposure and “lag 1” the effects after one day and so on. Due to high inter-lag correlations, “single lag” and “single MA” models were used to avoid collinearity-related errors. The results from the two models, one with MA and the other with single lags, were compared to test for the stochastic effects. Only the associations based on the MA (MA_2–7) model are reported in this study.

Notably, from bivariate analyses, there were significantly high correlations observed between different pollutant species and other covariates. High inter-pollutant correlations are mostly due to these pollutants having common major emission sources [19]. Thus, the health effects associated with the exposure to individual air pollutants were modeled separately in single-pollutant and single MA models, to avoid introducing errors associated with collinearity in the β estimates.

Additionally, the daily levels of the pollutant species and meteorological parameters displayed significant temporal variability [19]. From ANOVA, the variance of each of the covariates used varied significantly as a function of the mean, implying that the data was not homoscedastic. Also, from the analysis of residuals errors, the observed residuals were not normally distributed. In this case, we could not use a classical linear regression model since it assumes homoscedasticity (a constant variance in the data), and normally distributed residuals with a mean of zero, a condition that is almost impossible to meet with real life data such as those used in this study. With these data characteristics, the generalized linear time-series model (GLM) became the best choice. GLMs allow for modelling of the relationship between the exposure (independent variable) and the outcome (dependent variable), in a rather linear and additive way even though the underlying relationships may be non-linear and not additive. Therefore, since our data set was heteroscedastic, non-linear, and the residuals were not normally distributed, we modeled the exposure and the outcome with a GLM to make the relationship linear and concurrently stabilize the variance and normalize the errors/residuals.

The models were controlled for confounding due to the ambient temperature, relative humidity, and wind speed. Meteorology data were obtained online at https://www.wunderground.com (accessed on 3 April 2015). Daily and seasonal variations in the exposure–outcome association were accounted for by including the day of the week and month variables in the models [Equation (1)]. Confounding and effect modifications related to individual lifestyles (diet, exercise, smoking, etcetera) were not controlled due to limited data. Nevertheless, these variables do not change day-to-day and thus have a minimal effect on results from a time-series analysis. The analyses were further stratified by age group, gender, diagnosis category, and patient category. All the statistical analyses were completed using SAS 9.4 version. Our single pollutant, single MA model is shown in Equation (1).

(1)$\begin{array}{ll}\mathrm{l}\mathrm{n}\left({\mathrm{Y}}_{\mathrm{t}}\right)={\mathsf{\beta }}_0& +{\mathsf{\beta }}_1\left({\mathrm{M}\mathrm{A}}_{\mathrm{x}}-{\mathrm{P}\mathrm{S}}_{\mathrm{i}}\right)+{\mathsf{\beta }}_2\left(\mathrm{S}\mathrm{u}\mathrm{n}\right)+{\mathsf{\beta }}_3\left(\mathrm{M}\mathrm{o}\mathrm{n}\right)+{\mathsf{\beta }}_4\left(\mathrm{T}\mathrm{u}\mathrm{e}\right)+{\mathsf{\beta }}_5\left(\mathrm{W}\mathrm{e}\mathrm{d}\right)\\ & +{\mathsf{\beta }}_6\left(\mathrm{T}\mathrm{h}\mathrm{u}\right)+{\mathsf{\beta }}_7\left(\mathrm{F}\mathrm{r}\mathrm{i}\right)+{\mathsf{\beta }}_8\left(\mathrm{J}\mathrm{a}\mathrm{n}\right)+{\mathsf{\beta }}_9\left(\mathrm{F}\mathrm{e}\mathrm{b}\right)+{\mathsf{\beta }}_{10}\left(\mathrm{A}\mathrm{p}\mathrm{r}\right)\\ & +{\mathsf{\beta }}_{11}\left(\mathrm{M}\mathrm{a}\mathrm{y}\right)+{\mathsf{\beta }}_{12}\left(\mathrm{J}\mathrm{u}\mathrm{l}\right)+{\mathsf{\beta }}_{13}\left(\mathrm{A}\mathrm{u}\mathrm{g}\right)+{\mathsf{\beta }}_{14}\left(\mathrm{O}\mathrm{c}\mathrm{t}\right)\\ & +{\mathsf{\beta }}_{15}\left(\mathrm{M}\mathrm{e}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}.\right)+{\mathsf{\beta }}_{16}\left(\mathrm{H}\mathrm{u}\mathrm{m}\mathrm{i}\mathrm{d}\mathrm{i}\mathrm{t}\mathrm{y}\right)\\ & +{\mathsf{\beta }}_{17}\left(\mathrm{W}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{S}\mathrm{p}\mathrm{e}\mathrm{e}\mathrm{d}\right)\mathrm{E}\mathrm{q}.\left(1\right)\end{array}$

where

• ${\mathrm{Y}}_t$ = the daily hospital visits for either CVDs or RDs

• ${\mathrm{M}\mathrm{A}}_x$ = the individual moving averages (MA_2–7) for single pollutant and single MA models;

• ${\mathrm{P}\mathrm{S}}_i$ = the individual pollutant species (BC, p-SO₄²⁻, and p-NO₃⁻).

The relative risk (RR) estimates with 95% confidence intervals (CI) were determined based on 1.0 μg/m³ increases in daily BC and p-NO₃⁻, and 5.0 μg/m³ increases in p-SO₄²⁻. Thus, RR is the cardiopulmonary risk per 1.0 μg/m³ increase in daily BC, and p-NO₃⁻ and 5.0 μg/m³ increase in p-SO₄². The degree of statistical significance on the RR estimate was defined with a 95% CI and p-value. Statistically significant p-values were ≤0.05, and marginal statistical significances were 0.05 < p-value < 0.1. The RR was determined with combined diagnoses within each diagnosis category (CVDs or RDs). Combining diseases of the same diagnosis category prevents any misclassifications that may originate from wrong ICD-10 codes.

3. Results and Discussion

3.1. Hospital Data

3.1.1. Distribution of Hospital Data by Diagnosis, Patient Category, and Gender

More details of hospital data by gender, diagnosis, patient categories, and age groups have previously been published [34]. A total of 38,537 patient records were collected over the study period of which 21,014 (54.5%) were males and 17,523 (45.5%) were females. ER visits had 2274 CVDs and 18,401 RDs, outpatients 4520 CVDs and 10,327 RDs, and inpatients 1993 CVDs and 1022 RDs, as summarized in Table 2.

Most ER visits for CVDs were due to illnesses associated with ischemic heart disease (IHD) (66.6%) and hypertension (HTN) (25.1%). RDs were mostly diagnosed as respiratory disorder (RD) (66%), acute respiratory infections (A-RTIs) (16.7%), and asthma (12.5%).

Outpatients for CVDs were mainly due to illnesses related to HTN (68.9%) and IHD (28.3%). The majority of outpatient RDs were A-RTIs (71.7%), RD (15.5%), tonsillitis (6.8%) and asthma (3.8%).

Inpatient CVDs were primarily associated with HTN (70.4%), IHD (17.8%) and myocarditis (3.8%), while RDs were mainly due to A-RTIs (53.8%), pneumonia (16.8%), tonsillitis (13.8%), asthma (8.6%), and chronic RTIs (6.6%).

3.1.2. Distribution of the Hospital Data by Diagnosis, Patient Category, and Age Group

The relative distribution of hospital visits for both CVDs and RDs by patient category and age groups are summarized in Table 2. Inpatients were much fewer than the ER visits and outpatients for both CVDs and RDs, implying that the exposure to air pollution was associated with increased hospital visits but with less hospital admissions. This may also highlight the severity of the RDs and CVDs cases reported in the ER category, as more severe cases would have led to more hospital admissions (inpatients).

Across all patient categories, the prevalence of CVDs morbidity was highest among people who were 30+ years old with those above 45 years being the most vulnerable as shown in Table 1. CVDs-related health issues typically become more prevalent in old age [43] as most of the behavioral and lifestyle risks associated with cardiovascular health such as smoking [44,45], diet, and occupational exposures are experienced in adulthood. These exposures added to air pollution significantly increase the risk for cardiovascular morbidity and mortality. Elderly people are particularly more vulnerable due to the high prevalence of underlying cardiovascular diseases that may have not been diagnosed yet [43].

RDs morbidity was highest among people under 30 years with children 0–14 years being the most vulnerable (Table 2). Children are frequently active outdoors and have a high metabolism and an increased air intake which significantly increases their exposure to outdoor air pollution. Given that they are not little adults, their lungs and other biological systems are still undergoing rapid development. This significantly makes them more susceptible to the damaging effects of air pollution. The most frequent air pollution-related respiratory illnesses in children are associated with acute pulmonary infections, exacerbation, and the development of allergic reactions such as asthma. Notably, the relative distributions for both RDs and CVDs by age groups and patient categories were similar for both male and female patients.

3.2. Relative Risk (RR) Estimates for Cardiopulmonary Morbidity

RR was defined as the odds of developing a cardiovascular or respiratory disease following a specified increase in exposure to a given air pollutant. Since this is case-only data, the RR was computed by comparing the morbidity rates at different pollution levels. In studies with a control group, the RR may be determined by comparing the morbidity rate in the exposed group to the non-exposed.

The RR associated with a 5.0 μg/m³ increase in SO₄²⁻ and a 1.0 μg/m³ increase in BC and NO₃⁻ exposures were determined at 95% CIs. We based the unit of increment on the daily pollutants’ levels. Where the mean daily level was ≤10 μg/m³, the RR was based on a 1.0 μg/m³ increase, while a >10 μg/m³ RR was based on either a 5.0 μg/m³ or 10 μg/m³ increase depending on the daily mean and range.

The mean seasonal concentrations and range of BC and secondary aerosols were as shown in Table 1.

3.2.1. Cardiopulmonary Morbidity Risk Per 1.0 μg/m³ Increase in BC

• I. Combined Patient Categories

The overall RDs morbidity risk was highest at MA_2 but was marginally significant, 1.194 (CI: 0.974, 1.463). With gender stratified analysis, the risk became statistically significant in females, 1.272 (CI: 1.028, 1.575), as shown in Table 3 (Part A). No notable risk of CVDs was observed (data not presented).

By age group, the RDs morbidity risk was the highest in age group 0–14 years at all MAs but was only marginally significant at MA_2; 1.202 (CI: 0.953, 1.516). The risk was more in the male age group 31–45 years, especially at MAs 5–6; 1.407 (CI: 1.093, 1.904) to 1.472 (CI: 1.066, 2.034). The female RDs morbidity risk was the highest at age group 0–14 years; 1.243 (CI: 0.964, 1.603) to 1.368 (CI: 0.987, 1.895), with a statistical significance at MA_2, 1.273 (CI: 1.014, 1.598) as shown in Figure 1.

The CVDs morbidity risk increased sporadically with MAs but was not significant at all MAs (Figure S2).

• II. ER Visits

The RDs morbidity risk was the highest at MA_2; 1.114 (CI: 0.842, 1.4 73), and was comparable for both genders; male, 1.100 (CI: 0.836, 1.448), and female, 1.102 (CI: 0.839, 1.448) but with no statistical significance [Table 3 (part B)]. There was no observed risk of CVDs (data not presented).

By age group, the RDs morbidity risk was the highest in age groups below 45 years at MA_2 but with no statistical significance even with the gender stratified analysis as shown in Figure S3.

The risk of CVDs morbidity increased with MAs and especially in younger age-groups (≤30 years) but with no statistical significance even with gender stratified analysis as depicted in Figure S4. The occurrence of CVDs morbidity in children (if any) is usually associated with congenital anomalies, but the effect of BC exposure may not be excluded entirely.

• III. Outpatients

Overall, RDs increased with MAs, becoming marginally significant at MA 7; 1.326 (CI: 0.957, 1.838). The RDs morbidity risk was higher in females across all MAs; 1.290 (CI: 0.998, 1.667) to 1.544 (CI: 1.103, 2.162) and no risk was observed in males.

The overall CVDs morbidity risk was not statistically significant at all MAs and no risk was observed with the gender stratified analysis [Table 3 (part C)].

By age group, the RDs morbidity risk was the highest in age group 0–14 years and increased by MAs reaching 1.749 (CI: 1.160, 2.635) at MA 7. Females of 0–14 years were the most at risk for RDs morbidity across all MAs; 1.483 (CI: 1.035, 2.123) to 2.501 (CI: 1.522, 4.112). Among males RDs morbidity, the highest was in age-group 31–45 years and increased with MAs up to 1.895 (CI: 1.142, 3.145) at MA 7 (Figure 2).

The CVDs morbidity risk was the highest at 61–75 years across all MAs, but with no statistical significance. With gender stratified analysis, the CVDs morbidity risk was elevated in female age groups 15–30 and 76+ years, and the male age group 61–75 years across all MAs, but no statistical significance as shown in Figure S5.

• IV. Inpatients

The overall RDs morbidity risk was the highest at MA_2; 1.128 (CI: 0.892, 1.425) and males were more at risk; 1.226 (CI: 0.920, 1.633), but with no statistical significance. No RD morbidity risk was observed in females [Table 3 (part D)]. Also, no risk was observed for CVDs (data not presented).

By age group, the RDs morbidity risk was the highest in the male age group 0–14 years and was only statistically significant at MA 2; 1.415 (CI: 1.008, 1.985), as depicted in Supplemental Figure S6.

Estimates for the CVDs morbidity risk did not show any statistically significant associations for both males and females at all MAs as presented in Supplemental Figure S7.

3.2.2. Cardiopulmonary Morbidity Risk Per 5.0 μg/m³ Increase in p-SO₄²⁻

• I. Combined Patient Categories

The overall RDs risk increased with MA; 1.034 (CI: 0.970, 1.102) to 1.084 (CI: 0.993, 1.184) at MAs_2–7. Males were more at risk; 1.078 (CI: 1.011, 1.149) to 1.100 (CI: 1.028, 1.575) at MAs_3–7.

The risk of CVDs also increased with MAs, and was comparable for both genders: males, 1.049 (CI: 0.987, 1.115), and females, 1.057 (CI: 0.997, 1.121) at MA_7 [Table S1 (Part A)].

By age group, the risk of RDs was the highest at 15–30 years and increased with MAs; 1.070 (CI: 1.003, 1.140) to 1.097 (CI: 1.002, 1.201) at MAs_3–7. Males aged 31–45 years were the most at risk; 1.139 (CI: 1.064, 1.219) to 1.201 (CI: 1.105, 1.305) followed by age group 15–30 years; 1.067 (CI: 1.001, 1.137) to 1.107 (CI: 1.020, 1.201) at MAs_3–7. Among females, the estimate of the risk of RDs was the highest in age group 76+ years at MAs_2–4 with a marginal statistical significance (Figure 3).

Overall, the risk of CVDs was the highest in age group 15–30 years and increased with MAs; 1.075 (CI: 0.998, 1.157) to 1.109 (CI: 0.997, 1.234) at MAs 2–7. Males aged 31–45 years were the most at risk; 1.076 (CI: 1.012, 1.145) to 1.117 (CI: 1.042. 1.197) at MAs_4–7. Among females, the estimate of the risk of CVDs was the highest in age group 15–30 years but with no statistical significance (Figure 4).

• II. ER Visits

The overall RDs risk increased with MAs. The risk estimates were relatively higher among males than females, across all moving averages but with no statistical significance.

The overall CVDs risk also increased with MAs; 1.063 (CI: 1.010, 1.119) to 1.145 (CI: 1.066, 1.229) at MA_2–7, and was comparable across genders: male, 1.071 (CI: 1.016, 1.128) to 1.128 (CI: 1.049, 1.214), and female, 1.083 (CI: 1.025, 1.144) to 1.136 (CI: 1.057, 1.222) at MAs_2–7 [Table S1 (Part B)].

By age group, the estimate of the risk of RDs were the highest in age groups 15–30 years; 1.090 (CI: 0.999, 1.189) to 1.121 (CI: 1.001, 1.255) at MAs_3–7, and 46–60 years; 1.089 (CI: 1.025, 1.158) to 1.100 (CI: 1.007, 1.203) at MAs_2–7. High RDs risk estimates were also observed in age groups 0–14 and 61–75 years.

Females in age groups 15–30 and 46–60 years were the most at risk for RDs morbidity; 1.076 (CI: 1.004, 1.153) to 1.123 (CI: 1.020, 1.236) in age group 15–30 years, and 1.091 (CI: 1.018, 1.170) to 1.107 (CI: 1.015, 1.207) in age group 46–60 years at MAs_2–7 (Figure S8).

Overall, the risk of CVDs was the highest in age group 46–60 years and increased with MAs; 1.079 (CI: 1.013, 1.149) to 1.128 (CI: 1.039, 1.224) at MAs 3–7. Females aged 46–60 years were the most at risk; 1.072 (CI: 0.999, 1.150) to 1.121 (CI: 1.012. 1.242) at MAs_2–7 (Figure S9).

• III. Outpatients

The overall RDs risk was higher in males but with a marginal statistical significance; 1.078 (CI: 0.994, 1.169) to 1.087 (CI: 0.998, 1.183) at MAs_6–7. No major CVDs risk was observed [Table S1 (Part C)].

RDs risk estimates increased with MAs and were the highest in age groups 15–30 and 31–45 years; 1.079 (CI: 1.011, 1.153) to 1.095 (CI: 1.018, 1.178) at MAs_4–7 for age group 15–30 years, and 1.080 (CI: 0.980, 1.190) to 1.117 (CI: 1.004, 1.241) at MA_5–7 for age group 31–45 years. Males of 31–45 years were the most at risk; 1.173 (CI: 1.066, 1.291) to 1.256 (CI: 1.126, 1.400) at MAs_3–7. High RDs risk estimates were also the male age groups 15–30 and 46–75 years at MAs_2–7 as depicted in Figure 5.

Overall, the CVDs risk estimates were the highest in age group 31–45 years; 1.078 (CI: 1.004, 1.157) to 1.090 (CI: 1.010, 1.177) at MAs 4–7. Males aged 31–45 years were the most at risk; 1.111 (CI: 1.012, 1.220) to 1.120 (CI: 1.013, 1.239) at MAs 5–7 as shown in Figure S10.

• IV. Inpatients

Overall, the RDs risk estimates were statistically significant across all MAs; 1.063 (CI: 1.008, 1.122) to 1.080 (CI: 1.034, 1.129), and males were more at risk; 1.061 (CI: 1.000, 1.127) to 1.085 (CI: 1.032, 1.140).

The overall CVDs risk estimate increased with MAs, from 1.063 (CI: 1.020, 1.108) at MA 2 to 1.095 (CI: 1.034, 1.159) at MA 7, with more risk estimates observed in females; 1.091 (CI: 1.032, 1.152) to 1.137 (CI: 1.049, 1.233), as shown in Supplemental Table S1 (Part D).

By age group, the risk of RDs was the highest in age group 0–14 years at MAs_2–3; 1.060 (CI: 1.007, 1.115) to 1.073 (CI: 1.024, 1.124). Females of 0–14 years were the most at risk at MA_2–3; 1.64 (CI: 1.000, 1.132) to 1.082 (CI: 1.019, 1.149). Males of 0–14 years also had a high RDs risk at MA_2–3 (Figure S11).

The risk of CVDs was the highest at 31–45 years; 1.090 (CI: 0.977, 1.218) to 1.213 (CI: 1.021, 1.442) at MA_ 2–7. Females of 31–60 years were the most at risk; 1.075 (CI: 0.909, 1.272) to 1.137 (CI: 0.860, 1.504) at 31–45, and 1.084 (CI: 0.976, 1.204) to 1.106 (CI: 0.970, 1.261) at 46–60 (Figure S12).

3.2.3. Cardiopulmonary Morbidity Risk Per 1.0 μg/m³ Increase in p-NO₃⁻

• I. Combined Patient Categories, ER Visits, and In-Patients

There was no statistically significant risk for cardiopulmonary morbidity observed as illustrated in Supplemental Table S2 (Parts A, B, and D) and Figures S13–S16, S18, and S19. High risk estimates were observed for RDs morbidity in ER visits but with no statistical significance [Table S2 (Part B)].

• II. Outpatients

Overall, the CVDs morbidity risk was high at all of the MAs; 1.146 (CI: 0.975, 1.347) to 1.191 (CI: 1.015, 1.398) with relatively higher risk estimates observed in females; 1.141 (CI: 0.967, 1.347) to 1.212 (CI: 1.028, 1.429) than males; 1.146 (CI: 0.984, 1.334) to 1.184 (CI: 1.018, 1.376). No risk was observed for RD morbidity [Table S2 (Part C)].

By age group, the risk of RDs was the highest at 76+ years and increased with MAs but with no statistical significance as shown in Figure S16. The risk for CVDs morbidity was the highest in age group 61–75 years and increased with MAs; 1.204 (CI: 1.014, 1.429) to 1.316 (CI: 1.096, 1.579), with females being more at risk; 1.221 (CI: 0.987, 1.511) to 1.428 (CI: 1.123, 1.816) than males; 1.205 (CI: 1.022, 1.421) to 1.258 (CI: 1.052, 1.505) across all MAs (Figure 6).

3.3. Discussion

This is the first study to assess the cardiopulmonary risk associated with BC, p-SO₄²⁻, and p-NO₃⁻ exposure, in an urban population of Jeddah, Saudi Arabia. Most of the previous studies that assessed the health effects of air pollution, in particular BC, focused mainly on the elderly and those with pre-existing cardiopulmonary illnesses [9,10,12,46]. This study assessed the cardiopulmonary risk across all age groups, highlighting the risk associated with the exposure to individual pollutants in single-pollutant models. These results supplement our previously published data [34] on the exposure to overall PM_2.5 and cardiopulmonary health and further elucidate the dangers of the exposure to air pollution on the general population. Though our data was collected over a decade ago, it is notable that the daily levels of air pollution and the chemical composition, and consequently the health outcomes, have not changed significantly across the Middle East region over the past decade [47]. Anyway, this being one of the very first studies of its kind, the results from this study will contribute significantly towards bridging the existing knowledge gap and pave the way for more air pollution epidemiology studies in Saudi Arabia and the rest of the Middle East region.

Peng et al., (2009) [9] noted that the differences in epidemiologic evidence on the exposure to various PM_2.5 components (e.g., BC, p-SO₄²⁻, and p-NO₃⁻) and human health risk may be attributable to variations in the study areas, health outcomes studied, and the analytical methods used to measure these pollutants. This is true given the significant spatial–temporal variability regarding the levels and composition of air pollution. Also, the risk from the exposure to air pollution varies by the demographics (age, gender, ethnicity, socio-economic status, etcetera) of the study population. We kept this in perspective as we drew conclusions from our results on how they compare with data from Europe and North America.

For example, the ambient levels of PM_2.5 and its components measured in Europe and N. America are significantly lower than the levels typically observed in heavily industrialized and developing countries such as Saudi Arabia. Thus, the observed high cardiopulmonary risk may be attributable to the elevated air pollution levels as observed in this study. Notably, a dose–response relationship on the exposure and cardiopulmonary outcome is likely. More studies assessing exposure at elevated levels are still needed to confirm this association. Precisely, more similar studies (controlling for desert sandstorms) in the Middle East region will be critical to further understanding the data from this study.

• I. Black Carbon (BC)

An exposure to black carbon (BC) significantly increased the risk of cardiovascular diseases (CVDs) and respiratory diseases (RDs) across various age groups and patient categories. Female outpatients of 0–14 years (Figure 3) were particularly at the most at risk for RDs compared to males across all age groups. Among males, the RDs morbidity risk was the highest at 31–45 years. The disparity in the most at-risk age groups between males and females may highlight some differences in the exposure patterns by gender. While the males in the age group of 31–45 years are more likely to be exposed to BC emissions from heavy automobile traffic and occupationally from industrial emissions, the females in the age group 0–14 years are more likely to be exposed to BC residentially from cooking and other domestic applications. However, it should be noted that we did not control for the differences in exposure patterns by gender and occupation. Besides, we did not have any data on the indoor BC levels and thus we cannot substantiate this supposition. To further explore these findings, additional studies focusing on the cardiopulmonary morbidity risk from indoor BC exposure are recommended. Such research could provide more concrete evidence to either support or refine these initial findings and help develop targeted interventions for the most vulnerable population age groups.

CVD’s morbidity risk, on the other hand, was higher in age groups over 45 years. The elderly usually have pre-existing CVDs conditions that have not been diagnosed yet. The exposure to air pollution generally exacerbates these CVDs. Notably, most CVD risk estimates across age groups and patient categories were not statistically significant, which may be attributed to a smaller sample size.

Several studies have reported an increased cardiopulmonary risk associated with BC exposure. In a study by Mordukhovich et al., (2009) [48], a unit standard deviation increase in BC reportedly increased systolic BP by 1.5 mmHg increase [CI: 0.1, 2.8] and diastolic BP by 0.9 mmHg (CI: 0.2, 1.6). High BP is a major risk factor for coronary heart disease, stroke, and heart failure especially in people above 50 years [49,50]. Also, Zanobetti and Schwartz, (2006) [10] reported an increased risk of emergency hospitalization for myocardial infarction, 8.34% (CI: 0.21, 15.8) and pneumonia, 11.71% (CI: 4.79, 17.4) among the elderly (≥65 years) following the exposure to BC. Similarly, Peng et., (2009) [9], found that a unit increase in the IQR of BC was associated with a 0.80% (95% PI: 0.34–1.27%) increase in the risk of same-day admissions for CVD among Medicare clients above 65 years.

Our study results are consistent with data from these previous studies. The observed risk for CVDs and RDs is higher than reported in Europe and North America, which may be attributable to the high BC levels reported in this study.

• II. Particulate Sulfate (p-SO₄²⁻)

Existing toxicologic data indicate little to no evidence of a causal association between p-SO₄²⁻ exposure and health risk at ambient levels. Also, epidemiologic data on the health risk from the exposure to p-SO₄²⁻ are inconsistent [9,51]. However, our results differ and indicate a significantly increased cardiopulmonary morbidity risk across all age groups. The risk estimate for RD morbidity was high in almost all age groups, 15–30, 31–45, 46–60, and 61–75 years (Figure 4, Figure 6 and Figure S7), while the risk for CVD morbidity was the highest in age groups 31–45 and 46–60 years (Figure 5, Figures S8 and S9). Notably, the ambient levels of p-SO₄²⁻ observed in this study were much higher than those typically recorded in the cities of developed nations. Also, the cardiopulmonary morbidity risk was higher than previously reported at lower ambient p-SO₄²⁻ levels, alluding to a possible dose–response relationship. There were some risk disparities by age group and gender that may suggest some differences in exposure patterns. While males of a working age (20–60 years) are more likely to take on outdoors and industrial jobs where the exposure to p-SO₄²⁻ is higher, the females are more inclined to take on office jobs and/or stay at home which changes the dynamics in their exposure patterns. However, we can only suggest but cannot substantiate this observation, since we did not control for occupation in our models.

Burnett et al., (1995) [52], in their study over a 6-year period (1983–1988), reported that a 13.0 μg/m³ increase in p-SO₄²⁻ recorded on the day prior to admission was associated with a 3.70% (p < 0.0001) and 2.80% (p < 0.0001) increase in respiratory and cardiac admissions, respectively. Cardiac admissions also increased by age group: 2.50% for those < 65 years and 3.5% for those ≥ 65 years. Also, Chung et al., (2007) [53] reported a reduction in heart rate variability (HRV) following the exposure to p-SO₄²⁻. This association remained significant even after adjusting for OC and the overall PM_2.5 in three-pollutant models. Our study results are consistent with these previous studies and show a significantly higher risk for CVDs and RDs morbidity from the exposure to ambient p-SO₄²⁻. Also, the levels of ambient p-SO₄²⁻ in our study are much higher than that of the levels reported in the Burnett et al. (1995) [52] and Chuang et al., (2007) [53] studies, further alluding to a possible dose–response relationship.

• III. Particulate Nitrate (p-NO₃⁻)

The results show an increased CVD and RD risk, more so with CVDs. However, toxicologic and epidemiologic data on p-NO₃⁻ and cardiopulmonary health is very limited [51]. The literature search turned up just a few studies: one study by Peng et al., (2009) [9] that reported a 0.5% increase in CVD admissions on the same day per unit increase in the IQR of p-NO₃⁻ exposure, and a systematic review of long-term outdoor NO₂ exposure and mortality by Stieb et al., (2021) [54] where the long-term exposure to NO₂ was significantly associated with mortality from CVDs with a pooled hazard ratio (HR) of 1.058, 95%CI 1.026–1.091 per 10 ppb, lung cancer (HR 1.083, 95%CI 1.041–1.126), respiratory disease (HR 1.062, 95%CI1.035–1.089), and ischemic heart disease (HR 1.111, 95%CI 1.079–1.144). It is noteworthy that NO₂ is a precursor for the p-NO₃⁻ measured in PM_2.5. A higher cardiopulmonary risk is reported in this study, especially among outpatient CVDs (Figure 6). Despite the inconsistent toxicologic data on p-NO₃⁻ exposure and health risk [51], our results suggest a potentially increased risk. More epidemiologic and toxicologic studies particularly in regions with high ambient p-NO₃⁻ levels are needed to validate the existing toxicological data and supplement our findings.

3.4. Conclusions, Strengths, and Limitations

The results are consistent with previously reported data. However, the cardiopulmonary risk associated with BC, p-SO₄²⁻, and p-NO₃⁻ in this study is much higher. This is likely attributable to elevated pollution levels. Also, the cardiopulmonary risk was higher in all age groups, highlighting the damaging health effects of elevated air pollution.

Clearly, age and gender played a major role in the observed CVD and RD risk. The differences in the exposure patterns between men and women could explain one part of the puzzle. Culturally, outdoor casual and industrial jobs are more likely to be done by men which exposes them more to outdoor air pollution. This could be the reason for the high RD morbidity risk in males of 31–45 years following the exposure to p-SO₄²⁻. People below 30 years were more at risk for RDs with children of 0–14 years being the most vulnerable, especially for exposures to BC. Conversely, CVD morbidity was more prevalent in age groups above 30 years. CVDs tend to have longer incubation periods and are more prevalent in old age. This may be due to prolonged exposures to air pollution and lifestyles related to diet and exercise. The high CVD morbidity seen in age group 0–14 years (not statistically significant) may be more associated with congenital anomalies. Besides, these CVD risk estimates were based on a very small sample size.

The occasional higher cardiopulmonary morbidity in women could be more associated with differences in healthcare-seeking behaviors between men and women. Generally, women tend to be keener at seeking medical care in a timely manner.

There are several major strengths to this study. Firstly, we used real-time measurements of BC, p-SO₄²⁻, and p-NO₃⁻ with simultaneous collections of meteorological and hospital data for a whole year. Thus, we could control for the confounding and effect modifications from daily and seasonal variations in weather and air pollution on cardiopulmonary risk, in the statistical models. Also, with a whole year of sampling, we were able to amass a large dataset for both air quality and hospital data. This boosted the study statistical power and enabled more stratified analyses by age group, gender, and patient categories, to identify the most susceptible population sub-groups. Additionally, the study had a large catchment area (three fixed sampling sites) for the monitoring of air pollution (Figure S1), representing a mixture of industrial and residential areas. This allowed for a more accurate estimate of the exposure.

There were also some limitations to this study. Firstly, the exposures to BC, p-SO₄²⁻, and p-NO₃⁻ were not based on personal exposure estimates but ambient levels. Thus, there may be some errors in the estimates for personal exposure. However, we previously [34] noted that high correlations between ambient PM levels from fixed monitoring stations and personal exposure levels have been previously reported [55,56]. Also, PM data from fixed sampling sites, as seen in this study, can potentially introduce errors in the exposure estimates. This misclassification in exposure can severely underestimate the effects of air pollution [57]. To minimize this exposure error, we used multiple sampling sites. Other limitations may be associated with patients not living within the PM monitoring area. However, about 85% of Jeddah’s population is urban and live within the proximity of PM sampling areas. Also, with population-level risk estimates, comes huge potential for an ecological fallacy. Thus, the observed risk estimates may not apply at personal levels.

The results indicate that reducing the emissions of BC, and precursors of p-NO₃⁻ (NO_X) and p-SO₄²⁻ (SO₂) mostly from vehicular emissions (diesel engine trucks, buses), and industrial emissions (fossil fuels combustion), will potentially reduce the cardiopulmonary morbidity and mortality risk. Additionally, developing new materials and advanced technologies to minimize the large volume vehicular emissions of soot/BC and NO_X as seen in urban areas, will significantly lower the prevalence of the associated health outcomes. In a study by Choi et al., (2018) [58], they explored the introduction of an auxiliary device, a fuel activation device (FAD), to improve the combustion efficiency of internal engines and thus lower the emissions. The introduction of FADs significantly lowered the fuel consumption and ultimately reduced the emission levels. Such approaches are recommendable for adoption in Saudi Arabia and beyond to significantly lower the air pollution levels especially from vehicular emissions.

Notably, the assessment of health effects associated with the exposure to particulate air pollution has been mainly approached using the single-pollutant and single-lag models, adjusted for any potential confounders. However, air pollution comprises a complex mixture of chemical pollutants, and exposures to these pollutants occur simultaneously. The use of multi-pollutant models has been suggested in several studies [59,60,61,62,63]. However, multi-pollutant models are quite challenging and may require devising new and complex statistical approaches. Nevertheless, this approach may be ideal for providing a better understanding of the health risks associated with air pollution.

Clearly, more studies are still needed in this part of the world, to fully assess the influence of elevated air pollution on cardiopulmonary health. This is key to the formulation of more robust policies on air pollution regulation in the region and beyond.

Author Contributions

S.R.N. (Writing—Original draft, Visualization, and Data Curation); O.S.A. (Funding acquisition, and Project Administration); A.S. (Investigation and Methodology); D.O.C. (Supervision, Validation, and Writing- Review and Editing); H.M.M. (Validation and Methodology); J.Z. (Investigation and Methodology); A.J.A. (Investigation and Project Administration); and H.A.K. (Conceptualization; Writing—Review and Editing, and Supervision). All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Boards (Ethics Committees) of Saudi Arabia’s Ministry of Health, University at Albany, SUNY, and New York State Department of Health IRB committees.

Informed Consent Statement

Patient consent was waived since hospital data was not collected directly from patients, but rather as patient records coded with unique IDs to ensure confidentiality.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no competing interests.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. The risk of RDs from the exposure to BC by age group and moving average (MA)—combined patient categories.

Figure 2. The risk of RDs from the exposure to BC and by age group and moving average (MA)—outpatients.

Figure 3. The RDs morbidity risk from the exposure to p-SO42− by age group and moving average (MA)—combined patient categories.

Figure 4. The risk of CVDs from the exposure to p-SO42− by age groups and moving average (MA)—combined patient categories.

Figure 5. The RDs morbidity risk from the exposure to p-SO42− by age groups and moving average (MA)—outpatients.

Figure 6. CVD risk from the exposure to p-NO3− by age group and moving average (MA)—outpatients.

Table 1

The summarized 24 h PM_2.5, black carbon (BC), particulate sulfate (p-SO₄²⁻), particulate nitrate (p-NO₃⁻), and meteorology (temperature, relative humidity, and wind speed) per sampling cycle in Jeddah.

	Cycle_1			Cycle_2			Cycle_3			Cycle_4
Parameter	Mean ± S.D	Min	Max	Mean ± S.D	Min	Max	Mean ± S.D	Min	Max	Mean ± S.D	Min	Max
PM2.5 (μg/m3)	58.8 ± 25	22.3	118	36.2 ± 12.3	21.3	80	33.9 ± 9.1	18.5	63	38 ± 17.7	12.4	90.8
BCIR (μg/m3)	1.86 ± 0.62	0.76	2.93	1.80 ± 0.41	0.79	2.65	2.37 ± 0.58	1.40	4.0	2.28 ± 0.81	0.93	4.63
BCUV (μg/m3)	1.6 ± 0.5	0.7	2.6	1.2 ± 0.3	0.6	1.8	1.6 ± 0.3	0.1	2.3	1.6 ± 0.5	0.7	3.3
p-SO42− (μg/m3)	17.8 ± 17.0	2.92	85.9	8.51 ± 3.23	1.92	22.94	12.1 ± 4.94	4.56	29.33	8.6 ± 4.69	2.09	20.20
p-NO3− (μg/m3)	1.45 ± 0.9	0.12	4.14	1.12 ± 0.8	0.23	3.92	0.88 ± 0.55	0.09	2.27	1.16 ± 0.55	0.36	2.64
RH (%)	53.6 ± 8.1	38.0	71	56.2 ± 8.0	30.0	71	63.8 ± 6.8	47	78	51.9 ± 12	26	71.0
Temp. (°C)	29.6 ± 2.6	24.4	34.4	27.2 ± 0.8	31.1	35.6	29.6 ± 1.2	27.8	31.7	25 ± 1.8	20	27.8
WS (m/s)	6.5 ± 2.1	3.0	11	6.6 ± 1.5	4.0	10	5.4 ± 1.6	3.0	10	7.5 ± 3.0	3.0	15

Cycle_1 (Spring): 8 April–28 May 2013 (KAH, KFH, and Al-Rawda—only the first 10 days were sampled at this site), Cycle_2 (Summer): 11 July–24 August 2013 (KAH and KFH), Cycle_3 (Fall): 2 October–16 November 2013 (KAH and Al-Rehab/KAU), Cycle_4 (Winter): 7 January 2014–18 February 2014 (KAH and Al-Rehab/KAU); S.D: standard deviation. Note: Black carbon (BC) was measured at 2 wavelengths using a dual wavelength optical transmissometer. BC measurement at IR-wavelength is more specific to the actual BC/Soot and has been used for assessment in this study.

Table 2

Summary of the overall hospital data collected during the study period by age group and patient category—combined gender.

	ER Visits		Outpatients		Inpatients
	CVDs	RDs	CVDs	RDs	CVDs	RDs
Age Group †	N (%)	N (%)	N (%)	N (%)	N (%)	N (%)
0–14	159 (7)	11,263 (61.2)	26 (0.6)	6004 (58.1)	6 (0.3)	727 (71.1)
15–30	173 (7.6)	3164 (17.2)	134 (3.0)	1383 (13.4)	41 (2.1)	71 (6.9)
31–45	322 (14.2)	1979 (10.8)	698 (15.4)	1528 (14.8)	211 (10.6)	58 (5.7)
46–60	740 (32.5)	1223 (6.65)	2094 (46.3)	816 (7.9)	749 (37.6)	63 (6.2)
61–75	623 (27.4)	571 (3.1)	1292 (28.6)	445 (4.3)	686 (34.4)	70 (6.9)
76+	257 (11.3)	201 (1.1)	276 (6.1)	151 (1.5)	300 (15.1)	33 (3.2)
Gender
Female	896 (39.4)	8022 (43.6)	1891 (41.8)	5498 (53.2)	752 (37.7)	464 (45.4)
Male	1378 (60.6)	10,379 (56.4)	2629 (58.2)	4829 (46.8)	1241 (62.–3)	558 (54.6)
Total	2274	18,401	4520	10,327	1993	1022

^† Age group in years, CVDs—cardiovascular diseases, RDs—respiratory diseases.

Table 3

Relative risk (RR) estimates per 1.0 µg/m³ increase in the daily BC in Jeddah. Combined age groups.

Part A: Combined Patient Categories
RDs	(M+F)			Male (M)			Female (F)
	N	RR (95% CI)	p-Value	N	RR (95% CI)	p-Value	N	RR (95% CI)	p-Value
MA_2	207	1.194 (0.974, 1.463)	0.0875	206	1.108 (0.912, 1.348)	0.3022	207	1.272 (1.028, 1.575)	0.0269
MA_3	207	1.162 (0.925, 1.460)	0.1970	206	1.079 (0.865, 1.345)	0.5013	207	1.209 (0.953, 1.534)	0.1187
MA_4	207	1.105 (0.855, 1.427)	0.4459	206	0.973 (0.760, 1.245)	0.8247	207	1.183 (0.907, 1.545)	0.2156
MA_5	207	1.055 (0.804, 1.384)	0.6984	206	0.904 (0.696, 1.174)	0.4479	207	1.152 (0.868, 1.528)	0.3267
MA_6	207	1.021 (0.770, 1.353)	0.8851	206	0.887 (0.676, 1.163)	0.3850	207	1.112 (0.829, 1.491)	0.4788
MA_7	207	1.036 (0.775, 1.384)	0.8123	206	0.907 (0.685, 1.202)	0.4976	207	1.123 (0.831, 1.518)	0.4510
Part B: ER visits
RDs
MA_2	198	1.114 (0.842, 1.473)	0.4491	197	1.100 (0.836, 1.448)	0.4959	195	1.102 (0.839, 1.448)	0.4863
MA_3	198	1.038 (0.755, 1.428)	0.8190	197	1.043 (0.763, 1.425)	0.7939	195	1.009 (0.738, 1.380)	0.9531
Part C: Outpatients
RDs
MA_2	171	1.118 (0.876, 1.425)	0.3704	165	0.937 (0.735, 1.194)	0.5979	165	1.290 (0.998, 1.667)	0.0514
MA_3	171	1.089 (0.834, 1.422)	0.5311	165	0.955 (0.727, 1.256)	0.7430	165	1.210 (0.917, 1.596)	0.1782
MA_4	171	1.133 (0.851, 1.510)	0.3926	165	0.897 (0.667, 1.207)	0.4729	165	1.315 (0.977, 1.769)	0.0707
MA_5	171	1.194 (0.887, 1.606)	0.2425	165	0.925 (0.681, 1.257)	0.6180	165	1.379 (1.015, 1.874)	0.0398
MA_6	171	1.189 (0.874, 1.618)	0.2697	165	0.905 (0.656, 1.249)	0.5430	165	1.379 (1.003, 1.895)	0.0478
MA_7	171	1.326 (0.957, 1.838)	0.0903	165	0.983 (0.698, 1.385)	0.9224	165	1.544 (1.103, 2.162)	0.0115
CVDs
MA_4	150	1.046 (0.771, 1.418)	0.7748	141	1.005 (0.740, 1.364)	0.975	140	0.983 (0.719, 1.344)	0.9153
MA_5	150	1.046 (0.752, 1.455)	0.7907	141	0.961 (0.689, 1.339)	0.8127	140	0.963 (0.685, 1.354)	0.8286
MA_6	150	1.069 (0.751, 1.523)	0.7105	141	0.998 (0.695, 1.433)	0.9904	140	0.985 (0.683, 1.420)	0.9336
MA_7	150	1.075 (0.746, 1.550)	0.6976	141	0.989 (0.676, 1.445)	0.9526	140	0.997 (0.681, 1.461)	0.9887
Part D: Inpatients
RDs
MA_2	170	1.128 (0.892, 1.425)	0.3151	143	1.226 (0.920, 1.633)	0.1648	132	0.988 (0.748, 1.304)	0.9298
MA_3	170	1.061 (0.821, 1.371)	0.6511	143	1.082 (0.786, 1.489)	0.6291	132	0.919 (0.679, 1.244)	0.5832
MA_4	170	0.970 (0.731, 1.288)	0.8335	143	1.046 (0.733, 1.492)	0.8040	132	0.768 (0.545, 1.084)	0.1330

MA_2–MA_7 denote moving averages_2 to 7: MA_2 = mean (lags 0–1); MA_3 = mean (lags 0–2); MA_4 = mean (lags 0–3); MA_5 = mean (lags 0–4); MA_6 = mean (lags 0–5); and MA_7 = mean (lags 0–6).

Supplementary Materials

The following supplemental materials can be downloaded at: https://www.mdpi.com/article/10.3390/atmos16020168/s1, Figure S1: Map of Jeddah showing the air monitoring sites (King Abdul-Aziz Hospital site—KAH, King Fahad hospital site—KFH and King Abdul-Aziz University site—Al-Rehab/KAU); Figure S2: CVD risk from exposure to BC by age-group and moving average (MA)—combined patient categories. No significant risk; Figure S3: RD morbidity risk from exposure to BC by age-group and moving average (ER visits). No statistically significant risk; Figure S4: CVD morbidity risk from exposure to BC by age-group and moving average—ER visits. No statistically significant risk; Figure S5: CVD morbidity risk from exposure to BC by age-group and moving average—Outpatients. No statistically significant risk; Figure S6: RD morbidity risk from exposure to BC by age-group and moving average—Inpatients; Figure S7: CVD morbidity risk from exposure to BC by age-group and moving average—Inpatients. No statistically significant risk; Figure S8: RD morbidity risk from exposure to p-SO₄²⁻ by age-group and moving average (ER visits); Figure S9: CVD morbidity risk from exposure to p-SO₄²⁻ by age-group and moving average (ER visits); Figure S10: CVD morbidity risk from exposure to p-SO₄²⁻ by age-group and moving average (Outpatients); Figure S11: RD morbidity risk from exposure to p-SO₄²⁻ by age-group and moving average (Inpatients); Figure S12: CVD morbidity risk from exposure to p-SO₄²⁻ by age-group and moving average (Inpatients); Figure S13: RD risk from exposure to p-NO₃⁻ by age-group and moving average (MA)—combined patient categories. No statistically significant risk; Figure S14: CVD risk from exposure to p-NO₃⁻ by age-group and moving average (MA)—combined patient categories. No statistically significant risk; Figure S15: RD morbidity risk from exposure to p-NO₃⁻ by age-group and moving average (ER visits). No statistically significant RR estimates were observed; Figure S16: CVD morbidity risk from exposure to p-NO₃⁻ by age-group and moving average (ER visits). No statistically significant RR estimates were observed; Figure S17: RD risk from exposure to p-NO₃⁻ by age-group and moving average (MA)—Outpatients. No statistically significant risk; Figure S18: RD morbidity risk from exposure to p-NO₃⁻ by age-group and moving average (Inpatients). No statistically significant RR estimates were observed; Figure S19: CVD morbidity risk from exposure to p-NO₃⁻ by age-group and moving average (Inpatients). No statistically significant RR estimates were observed; Table S1: Cardiopulmonary morbidity relative risk (RR) estimates per 5.0 µg/m³ increase in daily p-SO₄²⁻ in Jeddah, Saudi Arabia. Combined age-groups; Table S2: Cardiopulmonary morbidity relative risk (RR) estimates per 1.0 µg/m³ increase in daily p-NO₃⁻ in Jeddah, Saudi Arabia. Combined age-groups.

References

1. World Health Organization (WHO). Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide-Global Update; 2005; Available online: http://whqlibdoc.who.int/hq/2006/WHO_SDE_PHE_OEH_06.02_eng.pdf (accessed on 18 December 2012).

2. Dutkiewicz, V.A.; Alvi, S.; Ghauri, B.M.; Choudhary, M.I.; Husain, L. Black carbon aerosols in urban air in South Asia. Atmos. Environ.; 2008; 43, pp. 1737-1744. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2008.12.043]

3. Ravindra, K.; Sokhi, R.; Vangrieken, R. Atmospheric polycyclic aromatic hydrocarbons: Source attribution, emission factors and regulation. Atmospheric Environ.; 2008; 42, pp. 2895-2921. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2007.12.010]

4. Galarneau, E. Source specificity and atmospheric processing of airborne PAHs: Implications for source apportionment. Atmos. Environ.; 2008; 42, pp. 8139-8149. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2008.07.025]

5. Vasilakos, C.; Levi, N.; Maggos, T.; Hatzianestis, J.; Michopoulos, J.; Helmis, C. Gas-particle concentration and characterization of sources of PAHs in the atmosphere of a suburban area in Athens, Greece. J. Hazard Mater; 2007; 140, pp. 45-51. [DOI: https://dx.doi.org/10.1016/j.jhazmat.2006.06.047]

6. Husain, L.; Dutkiewicz, V.A.; Khan, A.; Ghauri, B.M. Characterization of carbonaceous aerosols in urban air. Atmospheric Environ.; 2007; 41, pp. 6872-6883. [DOI: https://dx.doi.org/10.1016/j.atmosenv.2007.04.037]

7. Nichols, J.L.; Owens, E.O.; Dutton, S.J.; Luben, T.J. Systematic review of the effects of black carbon on cardiovascular disease among individuals with pre-existing disease. Int. J. Public Health; 2013; 58, pp. 707-724. [DOI: https://dx.doi.org/10.1007/s00038-013-0492-z]

8. Gehring, U.; Wijga, A.H.; Brauer, M.; Fischer, P.; de Jongste, J.C.; Kerkhof, M.; Oldenwening, M.; Smit, H.A.; Brunekreef, B. Traffic-related Air Pollution and the Development of Asthma and Allergies during the First 8 Years of Life. Am. J. Respir. Crit. Care Med.; 2010; 181, pp. 596-603. [DOI: https://dx.doi.org/10.1164/rccm.200906-0858OC]

9. Peng, R.D.; Bell, M.L.; Geyh, A.S.; McDermott, A.; Zeger, S.L.; Samet, J.M.; Dominici, F. Emergency Admissions for Cardiovascular and Respiratory Diseases and the Chemical Composition of Fine Particle Air Pollution. Environ. Health Perspect.; 2009; 117, pp. 957-963. [DOI: https://dx.doi.org/10.1289/ehp.0800185]

10. Zanobetti, A.; Schwartz, J. Air pollution and emergency admissions in Boston, MA. J. Epidemiol. Community Health; 2006; 60, pp. 890-895. [DOI: https://dx.doi.org/10.1136/jech.2005.039834]

11. Dockery, D.W.; Luttmann-Gibson, H.; Rich, D.Q.; Link, M.S.; Mittleman, M.A.; Gold, D.R.; Koutrakis, P.; Schwartz, J.D.; Verrier, R.L. Association of Air Pollution with Increased Incidence of Ventricular Tachyarrhythmias Recorded by Implanted Cardioverter Defibrillators. Environ. Health Perspect.; 2005; 113, pp. 670-674. [DOI: https://dx.doi.org/10.1289/ehp.7767]

12. Gold, D.R.; Litonjua, A.A.; Zanobetti, A.; Coull, B.A.; Schwartz, J.; MacCallum, G.; Verrier, R.L.; Nearing, B.D.; Canner, M.J.; Suh, H. et al. Air Pollution and ST-Segment Depression in Elderly Subjects. Environ. Health Perspect.; 2005; 113, pp. 883-887. [DOI: https://dx.doi.org/10.1289/ehp.7737] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16002377]

13. Kim, J.J.; Smorodinsky, S.; Lipsett, M.; Singer, B.C.; Hodgson, A.T.; Ostro, B. Traffic-related Air Pollution near Busy Roads. Am. J. Respir. Crit. Care Med.; 2004; 170, pp. 520-526. [DOI: https://dx.doi.org/10.1164/rccm.200403-281OC]

14. Cornell, A.G.; Chillrud, S.N.; Mellins, R.B.; Acosta, L.M.; Miller, R.L.; Quinn, J.W.; Yan, B.; Divjan, A.; Olmedo, O.E.; Lopez-Pintado, S. et al. Domestic airborne black carbon and exhaled nitric oxide in children in NYC. J. Expo. Sci. Environ. Epidemiol.; 2012; 22, pp. 258-266. [DOI: https://dx.doi.org/10.1038/jes.2012.3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22377682]

15. Lin, W.; Huang, W.; Zhu, T.; Hu, M.; Brunekreef, B.; Zhang, Y.; Liu, X.; Cheng, H.; Gehring, U.; Li, C. et al. Acute Respiratory Inflammation in Children and Black Carbon in Ambient Air before and during the 2008 Beijing Olympics. Environ. Health Perspect.; 2011; 119, pp. 1507-1512. [DOI: https://dx.doi.org/10.1289/ehp.1103461]

16. Panis, L. The effect of changing background emissions on external cost estimates for secondary particulates. Open Environ. Sci.; 2008; 2, pp. 47-53.

17. Harrison, R.M.; Yin, J. Particulate matter in the atmosphere: Which particle properties are important for its effects on health?. Sci. Total Environ.; 2000; 249, pp. 85-101. [DOI: https://dx.doi.org/10.1016/S0048-9697(99)00513-6]

18. Nayebare, S.R.; Aburizaiza, O.S.; Siddique, A.; Carpenter, D.O.; Zeb, J.; Aburizaiza, A.J.; Pantea, C.; Hussain, M.M.; Khwaja, H.A. Association of fine particulate air pollution with cardiopulmonary morbidity in Western Coast of Saudi Arabia. Saudi Med. J.; 2017; 38, pp. 905-912. [DOI: https://dx.doi.org/10.15537/smj.2017.9.18545]

19. Nayebare, S.R.; Aburizaiza, O.S.; Siddique, A.; Hussain, M.M.; Zeb, J.; Khatib, F.; Carpenter, D.O.; Blake, D.R.; Khwaja, H.A. Understanding the Sources of Ambient Fine Particulate Matter (PM_2.5) in Jeddah, Saudi Arabia. Atmosphere; 2022; 13, 711. [DOI: https://dx.doi.org/10.3390/atmos13050711]

20. World Health Organization (WHO). Ambient (Outdoor) Air Pollution. 2018; Available online: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health (accessed on 29 November 2024).

21. Landrigan, P.J.; Fuller, R.; Acosta, N.J.R.; Adeyi, O.; Arnold, R.; Basu, N.; Baldé, A.B.; Bertollini, R.; Bose-O’Reilly, S.; Boufford, J.I. et al. The Lancet Commission on pollution and health. Lancet; 2018; 391, pp. 462-512. [DOI: https://dx.doi.org/10.1016/S0140-6736(17)32345-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29056410]

22. The World Bank. The Cost of Air Pollution: Strengthening the Economic Case for Action. World Bank Group. 2016; Available online: https://www.worldbank.org/en/topic/environment/brief/the-cost-of-air-pollution (accessed on 29 November 2024).

23. Brauer, M.; Freedman, G.; Frostad, J.; van Donkelaar, A.; Martin, R.V.; Dentener, F.; van Dingenen, R.; Estep, K.; Amini, H.; Apte, J.S. et al. Ambient Air Pollution Exposure Estimation for the Global Burden of Disease 2013. Environ. Sci. Technol.; 2015; 50, pp. 79-88. [DOI: https://dx.doi.org/10.1021/acs.est.5b03709]

24. Pateraki, S.; Assimakopoulos, V.; Maggos, T.; Fameli, K.; Kotroni, V.; Vasilakos, C. Particulate matter pollution over a Mediterranean urban area. Sci. Total. Environ.; 2013; 463–464, pp. 508-524. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2013.05.076]

25. Brunekreef, B.; Beelen, R.; Hoek, G.; Schouten, L.; Bausch-Goldbohm, S.; Fischer, P.; Armstrong, B.; Hughes, E.; Jerrett, M.; van den Brandt, P. Effects of long-term exposure to traffic-related air pollution on respiratory and cardiovascular mortality in the Netherlands: The NLCS-AIR study. Res. Rep. Health Eff. Inst.; 2009; 139, pp. 5-71.

26. Brunekreef, B. Health effects of air pollution observed in cohort studies in Europe. J. Expo. Sci. Environ. Epidemiol.; 2007; 17, (Suppl. S2), pp. S61-S65. [DOI: https://dx.doi.org/10.1038/sj.jes.7500628]

27. Crouse, D.L.; Peters, P.A.; Van Donkelaar, A.; Goldberg, M.S.; Villeneuve, P.J.; Brion, O.; Khan, S.; Atari, D.O.; Jerrett, M.; Pope, C.A., III et al. Risk of Nonaccidental and Cardiovascular Mortality in Relation to Long-term Exposure to Low Concentrations of Fine Particulate Matter: A Canadian National-Level Cohort Study. Environ. Health Perspect.; 2012; 120, pp. 708-714. [DOI: https://dx.doi.org/10.1289/ehp.1104049]

28. Zanobetti, A.; Schwartz, J. The Effect of Fine and Coarse Particulate Air Pollution on Mortality: A National Analysis. Environ. Health Perspect.; 2009; 117, pp. 898-903. [DOI: https://dx.doi.org/10.1289/ehp.0800108]

29. Bell, M.L.; Ebisu, K.; Peng, R.D.; Walker, J.; Samet, J.M.; Zeger, S.L.; Dominici, F. Seasonal and Regional Short-term Effects of Fine Particles on Hospital Admissions in 202 US Counties, 1999–2005. Am. J. Epidemiol.; 2008; 168, pp. 1301-1310. [DOI: https://dx.doi.org/10.1093/aje/kwn252] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18854492]

30. Franklin, M.; Zeka, A.; Schwartz, J. Association between PM_2.5 and all-cause and specific-cause mortality in 27 US communities Association between PM2.5 and all-cause and specific-cause mortality in 27 US communities. J. Expos. Sci. Environ. Epidemiol.; 2007; 17, pp. 279-287. [DOI: https://dx.doi.org/10.1038/sj.jes.7500530] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17006435]

31. Laden, F.; Neas, L.M.; Dockery, D.W.; Schwartz, J. Association of Fine Particulate Matter from Different Sources with Daily Mortality in Six U.S. Cities. Environ. Health Perspect.; 2000; 108, 941. [DOI: https://dx.doi.org/10.1289/ehp.00108941]

32. Kamarehei, B.; Farhadi, M.; Soleimani, F.; Dolati, M.; Sepahvand, A.; Bayat, M.; Farhadi, A.; Sepahvand, A.; Mohammadi, M.J. The level, source, and health outcome of PM_2.5 exposure in Southwest Iran. Toxicol. Rep.; 2024; 13, 101730. [DOI: https://dx.doi.org/10.1016/j.toxrep.2024.101730] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39314232]

33. Abbasi-Kangevari, M.; Malekpour, M.-R.; Masinaei, M.; Moghaddam, S.S.; Ghamari, S.-H.; Abbasi-Kangevari, Z.; Rezaei, N.; Rezaei, N.; Mokdad, A.H.; Naghavi, M. et al. Effect of air pollution on disease burden, mortality, and life expectancy in North Africa and the Middle East: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Planet. Health; 2023; 7, pp. e358-e369. [DOI: https://dx.doi.org/10.1016/S2542-5196(23)00053-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37164512]

34. Nayebare, S.R.; Aburizaiza, O.S.; Siddique, A.; Carpenter, D.O.; Pope, C.A., III; Hussain, M.M.; Zeb, J.; Aburizaiza, A.J.; Khwaja, H.A. Fine particles exposure and cardiopulmonary morbidity in Jeddah: A time-series analysis. Sci. Total Environ.; 2018; 647, pp. 1314-1322. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2018.08.094] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30282325]

35. Malashock, D.; Khwaja, H.A.; Fatmi, Z.; Siddique, A.; Lu, Y.; Lin, S.; Carpenter, D. Short-term association between black carbon exposure and cardiovascular diseases in Pakistan’s largest megacity. Atmosphere; 2018; 9, 420. [DOI: https://dx.doi.org/10.3390/atmos9110420]

36. World Health Organization (WHO). Non-Communicable Diseases (NCD) Country Profiles; WHO: Geneva, Switzerland, 2014; Available online: https://www.who.int/countries/#S (accessed on 7 April 2016).

37. World Population Review—WPR. 2016; Available online: http://worldpopulationreview.com/countries/saudi-arabia-population/ (accessed on 7 April 2016).

38. PPW. Population Pyramids of the World (PPW) from 1950 to 2100. 2015; Available online: https://populationpyramid.net/saudi-arabia/2015/ (accessed on 7 April 2016).

39. SADP. Saudi Arabia Demographic Profile 2014. Available online: http://www.indexmundi.com/saudi_arabia/demographics_profile.html (accessed on 5 April 2016).

40. Nayebare, S.R.; Aburizaiza, O.S.; Khwaja, H.A.; Siddique, A.; Hussain, M.M.; Zeb, J.; Khatib, F.; Carpenter, D.O.; Blake, D.R. Chemical Characterization and Source Apportionment of PM_2.5 in Rabigh, Saudi Arabia. Aerosol Air Qual. Res.; 2016; 16, pp. 3114-3129. [DOI: https://dx.doi.org/10.4209/aaqr.2015.11.0658]

41. Baker, D.B.; Nieuwenhuijsen, M.J. Environmental Epidemiology: Study Methods and Application; Oxford University Press: Oxford, UK, 2008; pp. 172-182.

42. Zanobetti, A.; Schwartz, J.; Samoli, E.; Gryparis, A.; Touloumi, G.; Peacock, J.; Anderson, R.H.; Le Tertre, A.; Bobros, J.; Celko, M. et al. The temporal pattern of respiratory and heart disease mortality in response to air pollution. Environ. Health Perspect.; 2003; 111, pp. 1188-1193. [DOI: https://dx.doi.org/10.1289/ehp.5712] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12842772]

43. Ahmed, W.; Muhammad, T.; Maurya, C.; Akhtar, S.N. Prevalence and factors associated with undiagnosed and uncontrolled heart disease: A study based on self-reported chronic heart disease and symptom-based angina pectoris among middle-aged and older Indian adults. PLoS ONE; 2023; 18, e0287455. [DOI: https://dx.doi.org/10.1371/journal.pone.0287455]

44. Mons, U.; Müezzinler, A.; Gellert, C.; Schöttker, B.; Abnet, C.C.; Bobak, M.; de Groot, L.; Freedman, N.D.; Jansen, E.; Kee, F. et al. Impact of smoking and smoking cessation on cardiovascular events and mortality among older adults: Meta-analysis of individual participant data from prospective cohort studies of the CHANCES consortium. BMJ; 2015; 350, h1551. [DOI: https://dx.doi.org/10.1136/bmj.h1551]

45. Gopal, D.M.; Kalogeropoulos, A.P.; Georgiopoulou, V.V.; Smith, A.L.; Bauer, D.C.; Newman, A.B.; Kim, L.; Bibbins-Domingo, K.; Tindle, H.; Harris, T.B. et al. Cigarette smoking exposure and heart failure risk in older adults: The Health, Aging, and Body Composition Study. Am. Heart J.; 2012; 164, pp. 236-242. [DOI: https://dx.doi.org/10.1016/j.ahj.2012.05.013] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22877810]

46. Kirrane, E.; Luben, T.; Benson, A.; Owens, E.; Sacks, J.; Dutton, S.; Madden, M.; Nichols, J. A systematic review of cardiovascular responses associated with ambient black carbon and fine particulate matter. Environ. Int.; 2019; 127, pp. 305-316. [DOI: https://dx.doi.org/10.1016/j.envint.2019.02.027]

47. Rojas-Rueda, D.; Alsufyani, W.; Herbst, C.; AlBalawi, S.; Alsukait, R.; Alomran, M. Ambient particulate matter burden of disease in the Kingdom of Saudi Arabia. Environ. Res.; 2021; 197, 111036. [DOI: https://dx.doi.org/10.1016/j.envres.2021.111036] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33775683]

48. Mordukhovich, I.; Wilker, E.; Suh, H.; Wright, R.; Sparrow, D.; Vokonas, P.S.; Schwartz, J. Black Carbon Exposure, Oxidative Stress Genes, and Blood Pressure in a Repeated-Measures Study. Environ. Health Perspect.; 2009; 117, pp. 1767-1772. [DOI: https://dx.doi.org/10.1289/ehp.0900591] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20049130]

49. Chobanian, A.V.; Bakris, G.L.; Black, H.R.; Cushman, W.C.; Green, L.A.; Izzo, J.L., Jr. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: The JNC 7 Report. JAMA; 2003; 289, pp. 2560-2571. [DOI: https://dx.doi.org/10.1001/jama.289.19.2560]

50. James, P.A.; Oparil, S.; Carter, B.L.; Cushman, W.C.; Dennison-Himmelfarb, C.; Handler, J. 2014 evidence-based guideline for the management of high blood pressure in adults: Report from the panel members appointed to the eighth joint national committee (jnc 8). JAMA; 2014; 311, pp. 507-520. [DOI: https://dx.doi.org/10.1001/jama.2013.284427] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24352797]

51. Reiss, R.; Anderson, E.L.; Cross, C.E.; Hidy, G.; Hoel, D.; McClellan, R.; Moolgavkar, S. Evidence of Health Impacts of Sulfate-and Nitrate-Containing Particles in Ambient Air. Inhal. Toxicol.; 2007; 19, pp. 419-449. [DOI: https://dx.doi.org/10.1080/08958370601174941]

52. Burnett, R.T.; Dales, R.; Krewski, D.; Vincent, R.; Dann, T.; Brook, J.R. Associations between Ambient Particulate Sulfate and Admissions to Ontario Hospitals for Cardiac and Respiratory Diseases. Am. J. Epidemiol.; 1995; 142, pp. 15-22. [DOI: https://dx.doi.org/10.1093/oxfordjournals.aje.a117540] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/7785669]

53. Chuang, K.-J.; Chan, C.-C.; Su, T.-C.; Lin, L.-Y.; Lee, C.-T. Associations Between Particulate Sulfate and Organic Carbon Exposures and Heart Rate Variability in Patients with or at Risk for Cardiovascular Diseases. J. Occup. Environ. Med.; 2007; 49, pp. 610-617. [DOI: https://dx.doi.org/10.1097/JOM.0b013e318058205b] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17563603]

54. Stieb, D.M.; Berjawi, R.; Emode, M.; Zheng, C.; Salama, D.; Hocking, R.; Lyrette, N.; Matz, C.; Lavigne, E.; Shin, H.H. Systematic review and meta-analysis of cohort studies of long-term outdoor nitrogen dioxide exposure and mortality. PLoS ONE; 2021; 16, e0246451. [DOI: https://dx.doi.org/10.1371/journal.pone.0246451] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33539450]

55. Wilson, W.E.; Mage, D.T.; Grant, L.D. Estimating Separately Personal Exposure to Ambient and Nonambient Particulate Matter for Epidemiology and Risk Assessment: Why and How. J. Air Waste Manag. Assoc.; 2000; 50, pp. 1167-1183. [DOI: https://dx.doi.org/10.1080/10473289.2000.10464164] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10939210]

56. Janssen, N.A.; de Hartog, J.J.; Hoek, G.; Brunekreef, B.; Lanki, T.; Timonen, K.L.; Pekkanen, J. Personal Exposure to Fine Particulate Matter in Elderly Subjects: Relation between Personal, Indoor, and Outdoor Concentrations. J. Air Waste Manag. Assoc.; 2000; 50, pp. 1133-1143. [DOI: https://dx.doi.org/10.1080/10473289.2000.10464159]

57. Zeger, S.L.; Thomas, D.; Dominici, F.; Samet, J.M.; Schwartz, J.; Dockery, D.; Cohen, A. Exposure Measurement Error in Time-Series Studies of Air Pollution: Concepts and Consequences. Environ. Health Perspect.; 2000; 108, 419. [DOI: https://dx.doi.org/10.1289/ehp.00108419]

58. Choi, S.I.; Feng, J.P.; Seo, H.S.; Jo, Y.M.; Lee, H.C. Evaluation of the cavitation effect on liquid fuel atomization by numerical simulation. Korean J. Chem. Eng.; 2018; 35, pp. 2164-2171. [DOI: https://dx.doi.org/10.1007/s11814-018-0141-6]

59. Molitor, J.; Coker, E.; Jerrett, M.; Ritz, B.; Li, A. Health Review Committee. Part 3. Modeling of Multipollutant Profiles and Spatially Varying Health Effects with Applications to Indicators of Adverse Birth Outcomes. Res. Rep.; 2016; 183, Pt 3, pp. 3-47.

60. Billionnet, C.; Sherrill, D.; Annesi-Maesano, I. Estimating the Health Effects of Exposure to Multi-Pollutant Mixture. Ann. Epidemiol.; 2012; 22, pp. 126-141. [DOI: https://dx.doi.org/10.1016/j.annepidem.2011.11.004]

61. Johns, D.O.; Stanek, L.W.; Walker, K.; Benromdhane, S.; Hubbell, B.; Ross, M.; Devlin, R.B.; Costa, D.L.; Greenbaum, D.S. Practical Advancement of Multipollutant Scientific and Risk Assessment Approaches for Ambient Air Pollution. Environ. Health Perspect.; 2012; 120, pp. 1238-1242. [DOI: https://dx.doi.org/10.1289/ehp.1204939]

62. Vedal, S.; Kaufman, J.D. What does multi-pollutant air pollution research mean?. Am. J. Respir. Crit. Care Med.; 2011; 183, pp. 4-6. [DOI: https://dx.doi.org/10.1164/rccm.201009-1520ED]

63. Dominici, F.; Peng, R.D.; Barr, C.D.; Bell, M.L. Protecting Human Health from Air Pollution: Shifting from a Single-Pollutant to a Multi-pollutant Approach. Epidemiology; 2010; 21, pp. 187-194. [DOI: https://dx.doi.org/10.1097/EDE.0b013e3181cc86e8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20160561]